HIBAG (version 1.8.3)

hlaPublish: Finalize a HIBAG model

Description

Finalize a HIBAG model by removing unused SNP predictors and adding appendix information (platform, training set, authors, warning, etc)

Usage

hlaPublish(mobj, platform=NULL, information=NULL, warning=NULL, rm.unused.snp=TRUE, anonymize=TRUE, verbose=TRUE)

Arguments

mobj

an object of hlaAttrBagObj or hlaAttrBagClass

platform

the text of platform information

information

the other information, like authors

warning

any warning message

rm.unused.snp

if TRUE, remove unused SNPs from the model

anonymize

if TRUE, remove sample IDs

verbose

if TRUE, show information

Value

Returns a new object of hlaAttrBagObj.

See Also

hlaModelFromObj, hlaModelToObj

Examples

# make a "hlaAlleleClass" object
hla.id <- "A"
hla <- hlaAllele(HLA_Type_Table$sample.id,
    H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
    H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
    locus=hla.id, assembly="hg19")

# training genotypes
region <- 250   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position,
    hla.id, region*1000, assembly="hg19")
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id),
    samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id))


#
# train a HIBAG model
#
set.seed(1000)

# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hla, train.geno, nclassifier=2, verbose.detail=TRUE)
summary(model)
length(model$snp.id)

mobj <- hlaPublish(model,
    platform = "Illumina 1M Duo",
    information = "Training set -- HapMap Phase II")
model2 <- hlaModelFromObj(mobj)
length(mobj$snp.id)
mobj$appendix
summary(mobj)

p1 <- predict(model, train.geno)
p2 <- predict(model2, train.geno)

# check
cbind(p1$value, p2$value)